Dynamic and Targeted Allocation of Resources for Coaching Service

3. The method of claim 2, wherein a first resource includes a first time increment of the virtual coach that is allocated to the first user and a second resource includes a second time increment of the virtual coach that is allocated to the second user, the virtual coach being accessible by the plurality of users for a limited time period.

4. The method of claim 3, wherein a first activity indicates utilization of the coaching service during the first time increment by the first user and a second activity indicates utilization of the coaching service during the second time increment by the second user.

6. The method of claim 4, wherein the first activity and the second activity include user inputs responsive to a reoccurring polling process of the plurality of users by the coaching service.

8. The method of claim 3, wherein assignment of the first resource is dynamically reallocated to a target user of the plurality of users based on a physiological parameter of the target user monitored by a physiological monitoring device worn by the target user.

11. The method of claim 2, wherein the first user is a diabetic user and a first coaching protocol includes a diabetes management program for the diabetic user.

14. The method of claim 13, wherein the target user is identified based on an ongoing physiological measurement calculated by a sensor of a monitoring device worn by the target user.

16. The method of claim 13, wherein the target user is identified based on at least one of standard clinical data, a prescription profile, a risk profile including a risk score based on amount or frequency of related events, or a self-identified measure of confidence to manage a disease.

17. The method of claim 13, wherein the target user is identified based on an activity profile of the user including a level of engagement with the coaching service over a selected time increment.

18. The method of claim 13, wherein the allocation is based on a unique factor of the target user, sensor data associated with the target user, an availability of a resource, or a global factor of the plurality of users.

20. The server computer of claim 19, wherein the target coaching recipient is identified based on an activity profile of the coaching service.

21. The method of claim 1, wherein the machine learning algorithm includes a Naïve Bayes Classifier algorithm, a K Means Clustering algorithm, a Support Vector Machine algorithm, or an artificial neural network.